Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022


Koç U., SEZER E., Özkaya Y. A., Yarbay Y., Beşler M. S., Taydaş O., ...More

European Radiology, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1007/s00330-023-10391-y
  • Journal Name: European Radiology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Keywords: Abdomen, Artificial intelligence, Computer vision systems, Emergencies, X-ray tomography
  • Yıldız Technical University Affiliated: Yes

Abstract

Objectives: The artificial intelligence competition in healthcare at TEKNOFEST-2022 provided a platform to address the complex multi-class classification challenge of abdominal emergencies using computer vision techniques. This manuscript aimed to comprehensively present the methodologies for data preparation, annotation procedures, and rigorous evaluation metrics. Moreover, it was conducted to introduce a meticulously curated abdominal emergencies data set to the researchers. Methods: The data set underwent a comprehensive central screening procedure employing diverse algorithms extracted from the e-Nabız (Pulse) and National Teleradiology System of the Republic of Türkiye, Ministry of Health. Full anonymization of the data set was conducted. Subsequently, the data set was annotated by a group of ten experienced radiologists. The evaluation process was executed by calculating F 1 scores, which were derived from the intersection over union values between the predicted bounding boxes and the corresponding ground truth (GT) bounding boxes. The establishment of baseline performance metrics involved computing the average of the highest five F 1 scores. Results: Observations indicated a progressive decline in F 1 scores as the threshold value increased. Furthermore, it could be deduced that class 6 (abdominal aortic aneurysm/dissection) was relatively straightforward to detect compared to other classes, with class 5 (acute diverticulitis) presenting the most formidable challenge. It is noteworthy, however, that if all achieved outcomes for all classes were considered with a threshold of 0.5, the data set’s complexity and associated challenges became pronounced. Conclusion: This data set’s significance lies in its pioneering provision of labels and GT-boxes for six classes, fostering opportunities for researchers. Clinical relevance statement: The prompt identification and timely intervention in cases of emergent medical conditions hold paramount significance. The handling of patients’ care can be augmented, while the potential for errors is minimized, particularly amidst high caseload scenarios, through the application of AI. Key Points: • The data set used in artificial intelligence competition in healthcare (TEKNOFEST-2022) provides a 6-class data set of abdominal CT images consisting of a great variety of abdominal emergencies. • This data set is compiled from the National Teleradiology System data repository of emergency radiology departments of 459 hospitals. • Radiological data on abdominal emergencies is scarce in literature and this annotated competition data set can be a valuable resource for further studies and new AI models.